To scale complex financial products like Bank Statement and DSCR loans, processors were suffocating under manual data extraction. Highly paid underwriting teams were spending hours manually reading 40-page unstructured PDF bank statements, hand-calculating self-employed income, and separating business expenses from personal transfers. This manual bottleneck not only crushed turnaround times, but introduced severe legal, compliance, and calculation risks into the loan origination process. The mandate was clear: scale the origination volume without ballooning the operational headcount.
Automated Mortgage Underwriting
& Data Ingestion
The Bleeding Neck
Manual data extraction was crushing turnaround times.
Architecture & Prototype
Qeiva OCR Engine: Bank Statement Parser
1. Raw Input (Unstructured PDF Data)
Qeiva engineered and deployed a custom Python-based Optical Character Recognition (OCR) middleware pipeline. Instead of relying on a "Human API," this digital processor was built to automatically ingest raw, unstructured PDF financial documents. The backend architecture categorizes business deposits, filters out personal transfers, and calculates a verified 12-month income average. The pipeline then pushes this clean, structured data directly via API into the existing Loan Origination System (e.g., Encompass).
The ROI
0
Manual Data Entries
0
Calculation Risk
∞
Scalability (No Headcount)
Before: Processors spent an estimated 2.5 hours per loan manually reading 12 months of PDF bank statements.
After: The custom OCR pipeline processes the entire 40-page PDF package in under 45 seconds.
Net Impact: Reclaims approximately 25 hours per week, per processor. Cost-to-originate drops significantly.